# SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/2004.03696 · HTML: https://mlsanity.com/paper/2004.03696/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 30 of 71)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Efficient Medical Image Segmentation Based on Knowledge Distillation](https://mlsanity.com/paper/2108.09987/) (2021)
- [FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation](https://mlsanity.com/paper/2103.17235/) (2021)
- [WE-GS: An In-the-wild Efficient 3D Gaussian Representation for Unconstrained Photo Collections](https://mlsanity.com/paper/2406.02407/) (2024)
- [Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels](https://mlsanity.com/paper/2103.03451/) (2021)
- [Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model](https://mlsanity.com/paper/2409.04356/) (2024)
- [DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2310.12570/) (2023)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Feature Enhancer Segmentation Network (FES-Net) for Vessel Segmentation](https://mlsanity.com/paper/2309.03535/) (2023)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [Orientation and Context Entangled Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2207.11396/) (2022)
- [Channel prior convolutional attention for medical image segmentation](https://mlsanity.com/paper/2306.05196/) (2023)
- [Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation](https://mlsanity.com/paper/2203.08667/) (2022)
- [Explainable Diabetic Retinopathy Detection and Retinal Image Generation](https://mlsanity.com/paper/2107.00296/) (2021)
- [SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image](https://mlsanity.com/paper/2207.13882/) (2022)
- [LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images](https://mlsanity.com/paper/2309.05780/) (2023)
- [Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images](https://mlsanity.com/paper/2405.07338/) (2024)
- [CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation](https://mlsanity.com/paper/2108.12173/) (2021)
- [PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation](https://mlsanity.com/paper/2108.11695/) (2021)
- [Deep Learning for Iris Recognition: A Review](https://mlsanity.com/paper/2303.08514/) (2023)
- [EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation](https://mlsanity.com/paper/2107.01351/) (2021)
- [Retinal Vessel Segmentation with Pixel-wise Adaptive Filters](https://mlsanity.com/paper/2202.01782/) (2022)
- [Full-scale Representation Guided Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2501.18921/) (2025)
- [PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing](https://mlsanity.com/paper/2112.04552/) (2021)
- [L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors](https://mlsanity.com/paper/2203.16528/) (2022)
- [SCOPE: Structural Continuity Preservation for Medical Image Segmentation](https://mlsanity.com/paper/2304.14572/) (2023)
- [FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures](https://mlsanity.com/paper/2311.08059/) (2023)
- [Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization](https://mlsanity.com/paper/2406.14994/) (2024)
- [Retinal Vessel Segmentation with Deep Graph and Capsule Reasoning](https://mlsanity.com/paper/2409.11508/) (2024)
- [MDF-MLLM: Deep Fusion Through Cross-Modal Feature Alignment for Contextually Aware Fundoscopic Image Classification](https://mlsanity.com/paper/2509.21358/) (2025)

## Abstract (arXiv)

The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employs structured dropout convolutional blocks instead of the original convolutional blocks of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that the proposed SA-UNet achieves state-of-the-art performance on both datasets.The implementation and the trained networks are available on Github1.